{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "# Graph RAG: 图增强检索增强生成\n",
    "\n",
    "Graph RAG - 一种通过将知识组织为连接图来增强传统RAG系统的技巧，而不是将知识作为平面文档集合。这使得系统能够导航相关概念，并找到比标准向量相似性方法更符合上下文的相关信息。\n",
    "\n",
    "------\n",
    "Graph RAG的关键优势\n",
    "\n",
    "- 保留信息之间的关系\n",
    "- 通过连接的概念启用上下文的遍历\n",
    "- 提高处理复杂、多部分查询的能力\n",
    "- 通过可视化知识路径提供更好的解释性\n",
    "\n",
    "------\n",
    "实现步骤：\n",
    "- 从PDF中提取文本\n",
    "- 将提取的文本分割成重叠的块\n",
    "- 从文本块构建知识图谱，用模型从文本块中总结出关键概念，然后利用关键概念构建知识图谱节点，根据概念重叠和语义相似性计算边权重，设置边，从而创建知识图谱\n",
    "- 遍历知识图谱以找到与查询相关的信息，计算查询与所有节点之间的相似度，按相似度排序（降序），获取最相似的前 top-k 个节点作为起点，使用优先队列进行广度遍历，查找出与查询最相关的信息\n",
    "- 根据查询和相关块生成响应"
   ],
   "id": "a2bcb12e99d0b8f8"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-29T05:42:18.923259Z",
     "start_time": "2025-04-29T05:42:03.514773Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import fitz\n",
    "import os\n",
    "import re\n",
    "import json\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "from openai import OpenAI\n",
    "from dotenv import load_dotenv\n",
    "from datetime import datetime\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import heapq\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "import jieba\n",
    "from typing import List, Dict, Tuple, Any\n",
    "\n",
    "load_dotenv()"
   ],
   "id": "f86a5f0937b49eeb",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-29T05:42:19.222622Z",
     "start_time": "2025-04-29T05:42:18.953465Z"
    }
   },
   "cell_type": "code",
   "source": [
    "client = OpenAI(\n",
    "    base_url=os.getenv(\"LLM_BASE_URL\"),\n",
    "    api_key=os.getenv(\"LLM_API_KEY\")\n",
    ")\n",
    "llm_model = os.getenv(\"LLM_MODEL_ID\")\n",
    "embedding_model = os.getenv(\"EMBEDDING_MODEL_ID\")\n",
    "\n",
    "pdf_path = \"../../data/AI_Information.en.zh-CN.pdf\""
   ],
   "id": "2e9bdff05702fae",
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 文档处理函数",
   "id": "c50c2d68421a2d7e"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-29T05:42:19.455187Z",
     "start_time": "2025-04-29T05:42:19.449002Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def extract_text_from_pdf(pdf_path):\n",
    "    \"\"\"\n",
    "    从 PDF 文件中提取文本，\n",
    "\n",
    "    Args:\n",
    "        pdf_path (str): Path to the PDF file.\n",
    "\n",
    "    Returns:\n",
    "        str: Extracted text from the PDF.\n",
    "    \"\"\"\n",
    "    # 打开 PDF 文件\n",
    "    mypdf = fitz.open(pdf_path)\n",
    "    all_text = \"\"  # 初始化一个空字符串以存储提取的文本\n",
    "\n",
    "    # Iterate through each page in the PDF\n",
    "    for page_num in range(mypdf.page_count):\n",
    "        page = mypdf[page_num]\n",
    "        text = page.get_text(\"text\")  # 从页面中提取文本\n",
    "        all_text += text  # 将提取的文本追加到 all_text 字符串中\n",
    "\n",
    "    return all_text  # 返回提取的文本"
   ],
   "id": "d8697c643e812935",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-29T05:42:19.466256Z",
     "start_time": "2025-04-29T05:42:19.461232Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def chunk_text(text, chunk_size=1000, overlap=200):\n",
    "    \"\"\"\n",
    "    将文本分割为重叠的块。\n",
    "\n",
    "    Args:\n",
    "        text (str): 输入文本\n",
    "        chunk_size (int): 每个块的大小（以字符数为单位）\n",
    "        overlap (int): 块之间的重叠部分（以字符数为单位）\n",
    "\n",
    "    Returns:\n",
    "        List[Dict]: 包含元数据的分块列表\n",
    "    \"\"\"\n",
    "    chunks = []  # 初始化一个空列表来存储分块\n",
    "\n",
    "    # 使用步长(chunk_size - overlap)遍历文本\n",
    "    for i in range(0, len(text), chunk_size - overlap):\n",
    "        # 提取当前段落的文本\n",
    "        chunk_text = text[i:i + chunk_size]\n",
    "\n",
    "        # 确保不添加空块\n",
    "        if chunk_text:\n",
    "            # 将包含元数据的块追加到列表中\n",
    "            chunks.append({\n",
    "                \"text\": chunk_text,  # 文本内容\n",
    "                \"index\": len(chunks),  # 块的索引\n",
    "                \"start_pos\": i,  # 块在原始文本中的起始位置\n",
    "                \"end_pos\": i + len(chunk_text)  # 块在原始文本中的结束位置\n",
    "            })\n",
    "\n",
    "    # 打印创建的块数量\n",
    "    print(f\"Created {len(chunks)} text chunks\")\n",
    "\n",
    "    return chunks  # 返回块列表\n"
   ],
   "id": "f395d2a46dde09b3",
   "outputs": [],
   "execution_count": 4
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 创建嵌入",
   "id": "159e1f42b39fe466"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-29T05:42:19.481559Z",
     "start_time": "2025-04-29T05:42:19.475821Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def create_embeddings(texts):\n",
    "    \"\"\"\n",
    "    为给定文本创建嵌入向量。\n",
    "\n",
    "    Args:\n",
    "        texts (List[str]): 输入文本列表\n",
    "        model (str): 嵌入模型名称\n",
    "\n",
    "    Returns:\n",
    "        List[List[float]]: 嵌入向量列表\n",
    "    \"\"\"\n",
    "    # 处理空输入的情况\n",
    "    if not texts:\n",
    "        return []\n",
    "\n",
    "    # 分批次处理（OpenAI API 的限制）\n",
    "    batch_size = 100\n",
    "    all_embeddings = []\n",
    "\n",
    "    # 遍历输入文本，按批次生成嵌入\n",
    "    for i in range(0, len(texts), batch_size):\n",
    "        batch = texts[i:i + batch_size]  # 获取当前批次的文本\n",
    "\n",
    "        # 调用 OpenAI 接口生成嵌入\n",
    "        response = client.embeddings.create(\n",
    "            model=embedding_model,\n",
    "            input=batch\n",
    "        )\n",
    "\n",
    "        # 提取当前批次的嵌入向量\n",
    "        batch_embeddings = [item.embedding for item in response.data]\n",
    "        all_embeddings.extend(batch_embeddings)  # 将当前批次的嵌入向量加入总列表\n",
    "\n",
    "    return all_embeddings  # 返回所有嵌入向量\n"
   ],
   "id": "6113248e5d076b59",
   "outputs": [],
   "execution_count": 5
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 构建知识图谱",
   "id": "6fab114bb401e26e"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-29T05:42:19.497053Z",
     "start_time": "2025-04-29T05:42:19.490637Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def extract_concepts(text):\n",
    "    \"\"\"\n",
    "    从文本中提取关键概念。\n",
    "\n",
    "    Args:\n",
    "        text (str): 需要提取概念的文本\n",
    "\n",
    "    Returns:\n",
    "        List[str]: 包含提取出的概念的列表\n",
    "    \"\"\"\n",
    "    # 系统消息，用于指导模型执行任务\n",
    "    system_message = \"\"\"从提供的文本中提取关键概念和实体。\n",
    "只返回一个包含5到10个最重要的关键词、实体或概念的列表\n",
    "以JSON字符串数组的格式返回结果。\n",
    "\n",
    "结果格式为：{\"concepts\": [x, x, x]}\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "    # 调用OpenAI API进行请求\n",
    "    response = client.chat.completions.create(\n",
    "        model=llm_model,  # 指定使用的模型\n",
    "        messages=[\n",
    "            {\"role\": \"system\", \"content\": system_message},  # 系统消息\n",
    "            {\"role\": \"user\", \"content\": f\"从以下文本中提取关键概念:\\n\\n{text[:3000]}\"}  # 用户消息，限制文本长度以符合API要求\n",
    "        ],\n",
    "        temperature=0.0,  # 设置生成温度为确定性结果\n",
    "        response_format={\"type\": \"json_object\"}  # 指定响应格式为JSON对象\n",
    "    )\n",
    "\n",
    "    try:\n",
    "        # 从响应中解析概念\n",
    "        concepts_json = json.loads(response.choices[0].message.content.strip())  # 将响应内容解析为JSON\n",
    "        concepts = concepts_json.get(\"concepts\", [])  # 获取\"concepts\"字段的值\n",
    "        if not concepts and \"concepts\" not in concepts_json:\n",
    "            # 如果未找到\"concepts\"字段，则尝试获取JSON中的任意列表\n",
    "            for key, value in concepts_json.items():\n",
    "                if isinstance(value, list):\n",
    "                    concepts = value\n",
    "                    break\n",
    "        return concepts  # 返回提取出的概念列表\n",
    "    except (json.JSONDecodeError, AttributeError):\n",
    "        # 如果JSON解析失败，则进行回退处理\n",
    "        content = response.choices[0].message.content  # 获取原始响应内容\n",
    "        # 尝试从响应内容中提取类似列表的部分\n",
    "        matches = re.findall(r'\\[(.*?)\\]', content, re.DOTALL)  # 查找方括号内的内容\n",
    "        if matches:\n",
    "            items = re.findall(r'\"([^\"]*)\"', matches[0])  # 提取方括号内的字符串项\n",
    "            return items\n",
    "        return []  # 如果无法提取，则返回空列表\n"
   ],
   "id": "691fdcacfc7972a7",
   "outputs": [],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-29T05:42:19.516195Z",
     "start_time": "2025-04-29T05:42:19.508578Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def build_knowledge_graph(chunks):\n",
    "    \"\"\"\n",
    "    从文本片段构建知识图谱。\n",
    "\n",
    "    Args:\n",
    "        chunks (List[Dict]): 包含元数据的文本片段列表\n",
    "\n",
    "    Returns:\n",
    "        Tuple[nx.Graph, List[np.ndarray]]: 知识图谱和片段嵌入\n",
    "    \"\"\"\n",
    "    print(\"正在构建知识图谱...\")\n",
    "\n",
    "    # 创建一个图\n",
    "    graph = nx.Graph()\n",
    "\n",
    "    # 提取片段文本\n",
    "    texts = [chunk[\"text\"] for chunk in chunks]\n",
    "\n",
    "    # 为所有片段创建嵌入\n",
    "    print(\"正在为片段创建嵌入...\")\n",
    "    embeddings = create_embeddings(texts)\n",
    "\n",
    "    # 将节点添加到图中\n",
    "    print(\"正在将节点添加到图中...\")\n",
    "    for i, chunk in enumerate(chunks):\n",
    "        # 从片段中提取概念\n",
    "        print(f\"正在从片段 {i+1}/{len(chunks)} 中提取概念...\")\n",
    "        concepts = extract_concepts(chunk[\"text\"])\n",
    "\n",
    "        # 添加带有属性的节点\n",
    "        graph.add_node(i,\n",
    "                      text=chunk[\"text\"],\n",
    "                      concepts=concepts,\n",
    "                      embedding=embeddings[i])\n",
    "\n",
    "    # 根据共享概念连接节点\n",
    "    print(\"正在在节点之间创建边...\")\n",
    "    for i in range(len(chunks)):\n",
    "        node_concepts = set(graph.nodes[i][\"concepts\"])\n",
    "\n",
    "        for j in range(i + 1, len(chunks)):\n",
    "            # 计算概念重叠\n",
    "            other_concepts = set(graph.nodes[j][\"concepts\"])\n",
    "            shared_concepts = node_concepts.intersection(other_concepts)    # 两个节点之间交集\n",
    "\n",
    "            # 如果它们共享概念，则添加一条边\n",
    "            if shared_concepts:\n",
    "                # 使用嵌入计算语义相似性\n",
    "                similarity = np.dot(embeddings[i], embeddings[j]) / (np.linalg.norm(embeddings[i]) * np.linalg.norm(embeddings[j]))\n",
    "\n",
    "                # 根据概念重叠和语义相似性计算边权重\n",
    "                concept_score = len(shared_concepts) / min(len(node_concepts), len(other_concepts))\n",
    "                edge_weight = 0.7 * similarity + 0.3 * concept_score\n",
    "\n",
    "                # 仅添加具有显著关系的边\n",
    "                if edge_weight > 0.6:\n",
    "                    graph.add_edge(i, j,\n",
    "                                  weight=edge_weight,\n",
    "                                  similarity=similarity,\n",
    "                                  shared_concepts=list(shared_concepts))\n",
    "\n",
    "    print(f\"知识图谱已构建，包含 {graph.number_of_nodes()} 个节点和 {graph.number_of_edges()} 条边\")\n",
    "    return graph, embeddings\n"
   ],
   "id": "1adad3fa85c99ae0",
   "outputs": [],
   "execution_count": 7
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 图形遍历与查询处理\n",
   "id": "a122a976001e97a4"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-29T05:42:19.535747Z",
     "start_time": "2025-04-29T05:42:19.525544Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def traverse_graph(query, graph, embeddings, top_k=5, max_depth=3):\n",
    "    \"\"\"\n",
    "    遍历知识图谱以查找与查询相关的信息。\n",
    "\n",
    "    Args:\n",
    "        query (str): 用户的问题\n",
    "        graph (nx.Graph): 知识图谱\n",
    "        embeddings (List): 节点嵌入列表\n",
    "        top_k (int): 考虑的初始节点数量\n",
    "        max_depth (int): 最大遍历深度\n",
    "\n",
    "    Returns:\n",
    "        List[Dict]: 图遍历得到的相关信息\n",
    "    \"\"\"\n",
    "    print(f\"正在为查询遍历图: {query}\")\n",
    "\n",
    "    # 获取查询的嵌入\n",
    "    query_embedding = create_embeddings(query)\n",
    "\n",
    "    # 计算查询与所有节点之间的相似度\n",
    "    similarities = []\n",
    "    for i, node_embedding in enumerate(embeddings):\n",
    "        similarity = np.dot(query_embedding, node_embedding) / (np.linalg.norm(query_embedding) * np.linalg.norm(node_embedding))\n",
    "        similarities.append((i, similarity))\n",
    "\n",
    "    # 按相似度排序（降序）\n",
    "    similarities.sort(key=lambda x: x[1], reverse=True)\n",
    "\n",
    "    # 获取最相似的前 top-k 个节点作为起点\n",
    "    starting_nodes = [node for node, _ in similarities[:top_k]]\n",
    "    print(f\"从 {len(starting_nodes)} 个节点开始遍历\")\n",
    "\n",
    "    # 初始化遍历\n",
    "    visited = set()  # 用于跟踪已访问节点的集合\n",
    "    traversal_path = []  # 存储遍历路径的列表\n",
    "    results = []  # 存储结果的列表\n",
    "\n",
    "    # 使用优先队列进行遍历\n",
    "    queue = []\n",
    "    for node in starting_nodes:\n",
    "        heapq.heappush(queue, (-similarities[node][1], node))  # 负号用于最大堆\n",
    "\n",
    "    # 使用修改后的基于优先级的广度优先搜索遍历图\n",
    "    while queue and len(results) < (top_k * 3):  # 将结果限制为 top_k * 3\n",
    "        _, node = heapq.heappop(queue)\n",
    "\n",
    "        if node in visited:\n",
    "            continue\n",
    "\n",
    "        # 标记为已访问\n",
    "        visited.add(node)\n",
    "        traversal_path.append(node)\n",
    "\n",
    "        # 将当前节点的文本添加到结果中\n",
    "        results.append({\n",
    "            \"text\": graph.nodes[node][\"text\"],\n",
    "            \"concepts\": graph.nodes[node][\"concepts\"],\n",
    "            \"node_id\": node\n",
    "        })\n",
    "\n",
    "        # 如果尚未达到最大深度，则探索邻居\n",
    "        if len(traversal_path) < max_depth:\n",
    "            neighbors = [(neighbor, graph[node][neighbor][\"weight\"])\n",
    "                        for neighbor in graph.neighbors(node)\n",
    "                        if neighbor not in visited]\n",
    "\n",
    "            # 根据边权重将邻居添加到队列中\n",
    "            for neighbor, weight in sorted(neighbors, key=lambda x: x[1], reverse=True):\n",
    "                heapq.heappush(queue, (-weight, neighbor))\n",
    "\n",
    "    print(f\"图遍历找到了 {len(results)} 个相关片段\")\n",
    "    return results, traversal_path\n"
   ],
   "id": "8e70eccad0d9f405",
   "outputs": [],
   "execution_count": 8
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 回答生成",
   "id": "4bc9c5b34f159ae0"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-29T05:42:19.552060Z",
     "start_time": "2025-04-29T05:42:19.545619Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def generate_response(query, context_chunks):\n",
    "    \"\"\"\n",
    "    根据检索到的上下文生成回答。\n",
    "\n",
    "    Args:\n",
    "        query (str): 用户的问题\n",
    "        context_chunks (List[Dict]): 从图遍历中获取的相关片段\n",
    "\n",
    "    Returns:\n",
    "        str: 生成的回答\n",
    "    \"\"\"\n",
    "    # 从每个相关片段中提取文本内容\n",
    "    context_texts = [chunk[\"text\"] for chunk in context_chunks]\n",
    "\n",
    "    # 将提取的文本片段合并为一个单一的上下文字符串，片段之间用 \"---\" 分隔\n",
    "    combined_context = \"\\n\\n---\\n\\n\".join(context_texts)\n",
    "\n",
    "    # 定义上下文的最大允许长度（OpenAI 的限制）\n",
    "    max_context = 14000\n",
    "\n",
    "    # 如果合并后的上下文长度超过最大限制，则进行截断\n",
    "    if len(combined_context) > max_context:\n",
    "        combined_context = combined_context[:max_context] + \"... [truncated]\"\n",
    "\n",
    "    # 定义系统消息以指导 AI 助手\n",
    "    system_message = \"\"\"你是一个有用的 AI 助手。根据提供的上下文回答用户的问题。\n",
    "如果上下文中没有相关信息，请说明。尽可能在回答中引用上下文的具体部分。\"\"\"\n",
    "\n",
    "    # 使用 OpenAI API 生成回答\n",
    "    response = client.chat.completions.create(\n",
    "        model=llm_model,  # 指定使用的模型\n",
    "        messages=[\n",
    "            {\"role\": \"system\", \"content\": system_message},  # 系统消息以指导助手\n",
    "            {\"role\": \"user\", \"content\": f\"上下文:\\n{combined_context}\\n\\n问题: {query}\"}  # 用户消息包含上下文和问题\n",
    "        ],\n",
    "        temperature=0.2  # 设置回答生成的温度参数\n",
    "    )\n",
    "\n",
    "    # 返回生成的回答内容\n",
    "    return response.choices[0].message.content\n"
   ],
   "id": "fb1666f56ec154f",
   "outputs": [],
   "execution_count": 9
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 可视化",
   "id": "15bf0bb548653279"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-29T05:55:11.365709Z",
     "start_time": "2025-04-29T05:55:11.354576Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def visualize_graph_traversal(graph, traversal_path):\n",
    "    \"\"\"\n",
    "    可视化知识图谱和遍历路径。\n",
    "\n",
    "    Args:\n",
    "        graph (nx.Graph): 知识图谱\n",
    "        traversal_path (List): 遍历顺序中的节点列表\n",
    "    \"\"\"\n",
    "    # 添加以下两行配置，解决中文显示问题\n",
    "    plt.rcParams['font.sans-serif'] = ['SimHei']  # 使用黑体显示中文\n",
    "    plt.rcParams['axes.unicode_minus'] = False    # 解决负号 '-' 显示为方块的问题\n",
    "\n",
    "    plt.figure(figsize=(12, 10))  # 设置图形大小为 12x10\n",
    "\n",
    "    # 定义节点颜色，默认为浅蓝色\n",
    "    node_color = ['lightblue'] * graph.number_of_nodes()\n",
    "\n",
    "    # 将遍历路径中的节点高亮为浅绿色\n",
    "    for node in traversal_path:\n",
    "        node_color[node] = 'lightgreen'\n",
    "\n",
    "    # 将起始节点标记为绿色，结束节点标记为红色\n",
    "    if traversal_path:\n",
    "        node_color[traversal_path[0]] = 'green'  # 起始节点\n",
    "        node_color[traversal_path[-1]] = 'red'  # 结束节点\n",
    "\n",
    "    # 使用弹簧布局创建所有节点的位置\n",
    "    pos = nx.spring_layout(graph, k=0.5, iterations=50, seed=42)\n",
    "\n",
    "    # 绘制图谱节点\n",
    "    nx.draw_networkx_nodes(graph, pos, node_color=node_color, node_size=500, alpha=0.8)\n",
    "\n",
    "    # 绘制边，宽度与权重成正比\n",
    "    for u, v, data in graph.edges(data=True):\n",
    "        weight = data.get('weight', 1.0)  # 获取边的权重，默认值为 1.0\n",
    "        nx.draw_networkx_edges(graph, pos, edgelist=[(u, v)], width=weight*2, alpha=0.6)\n",
    "\n",
    "    # 使用红色虚线绘制遍历路径\n",
    "    traversal_edges = [(traversal_path[i], traversal_path[i+1])\n",
    "                      for i in range(len(traversal_path)-1)]\n",
    "\n",
    "    nx.draw_networkx_edges(graph, pos, edgelist=traversal_edges,\n",
    "                          width=3, alpha=0.8, edge_color='red',\n",
    "                          style='dashed', arrows=True)\n",
    "\n",
    "    # 为每个节点添加标签，显示第一个概念\n",
    "    labels = {}\n",
    "    for node in graph.nodes():\n",
    "        concepts = graph.nodes[node]['concepts']  # 获取节点的概念列表\n",
    "        label = concepts[0] if concepts else f\"Node {node}\"  # 如果没有概念，则显示节点编号\n",
    "        labels[node] = f\"{node}: {label}\"\n",
    "\n",
    "    nx.draw_networkx_labels(graph, pos, labels=labels, font_size=8)  # 绘制节点标签\n",
    "\n",
    "    plt.title(\"知识图谱与遍历路径\")  # 设置图表标题\n",
    "    plt.axis('off')  # 关闭坐标轴\n",
    "    plt.tight_layout()  # 调整布局\n",
    "    plt.show()  # 显示图表\n"
   ],
   "id": "38d89aef953b8916",
   "outputs": [],
   "execution_count": 15
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 完整的 Graph RAG 流程",
   "id": "76d5e3ad880333f0"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-29T05:42:19.580107Z",
     "start_time": "2025-04-29T05:42:19.575396Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def graph_rag_pipeline(pdf_path, query, chunk_size=1000, chunk_overlap=200, top_k=3):\n",
    "    \"\"\"\n",
    "    完整的基于图的RAG（检索增强生成）管道，从文档到答案。\n",
    "\n",
    "    Args:\n",
    "        pdf_path (str): PDF文档的路径\n",
    "        query (str): 用户的问题\n",
    "        chunk_size (int): 文本块的大小\n",
    "        chunk_overlap (int): 块之间的重叠量\n",
    "        top_k (int): 用于遍历的顶级节点数量\n",
    "\n",
    "    Returns:\n",
    "        Dict: 包括答案和图可视化数据的结果\n",
    "    \"\"\"\n",
    "    # 从PDF文档中提取文本\n",
    "    text = extract_text_from_pdf(pdf_path)\n",
    "\n",
    "    # 将提取的文本分割成重叠的块\n",
    "    chunks = chunk_text(text, chunk_size, chunk_overlap)\n",
    "\n",
    "    # 从文本块构建知识图谱\n",
    "    graph, embeddings = build_knowledge_graph(chunks)\n",
    "\n",
    "    # 遍历知识图谱以找到与查询相关的信息\n",
    "    relevant_chunks, traversal_path = traverse_graph(query, graph, embeddings, top_k)\n",
    "\n",
    "    # 根据查询和相关块生成响应\n",
    "    response = generate_response(query, relevant_chunks)\n",
    "\n",
    "    # 可视化图遍历路径\n",
    "    visualize_graph_traversal(graph, traversal_path)\n",
    "\n",
    "    # 返回查询、响应、相关块、遍历路径和图\n",
    "    return {\n",
    "        \"query\": query,\n",
    "        \"response\": response,\n",
    "        \"relevant_chunks\": relevant_chunks,\n",
    "        \"traversal_path\": traversal_path,\n",
    "        \"graph\": graph\n",
    "    }\n"
   ],
   "id": "52e5b9b011df9bb7",
   "outputs": [],
   "execution_count": 11
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 评估函数",
   "id": "b332f018cf711a6"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-29T05:42:19.600106Z",
     "start_time": "2025-04-29T05:42:19.591539Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def evaluate_graph_rag(pdf_path, test_queries, reference_answers=None):\n",
    "    \"\"\"\n",
    "    评估基于图的RAG（Graph RAG）在多个测试问题上的表现。\n",
    "\n",
    "    Args:\n",
    "        pdf_path (str): PDF文档的路径\n",
    "        test_queries (List[str]): 测试问题列表\n",
    "        reference_answers (List[str], 可选): 用于对比的参考答案列表\n",
    "\n",
    "    Returns:\n",
    "        Dict: 评估结果\n",
    "    \"\"\"\n",
    "    # 从PDF中提取文本\n",
    "    text = extract_text_from_pdf(pdf_path)\n",
    "\n",
    "    # 将文本分割为片段\n",
    "    chunks = chunk_text(text)\n",
    "\n",
    "    # 构建知识图谱（针对所有问题仅构建一次）\n",
    "    graph, embeddings = build_knowledge_graph(chunks)\n",
    "\n",
    "    results = []\n",
    "\n",
    "    # 遍历每个测试问题\n",
    "    for i, query in enumerate(test_queries):\n",
    "        print(f\"\\n\\n=== 正在评估问题 {i+1}/{len(test_queries)} ===\")\n",
    "        print(f\"问题: {query}\")\n",
    "\n",
    "        # 在图中遍历以找到相关的信息\n",
    "        relevant_chunks, traversal_path = traverse_graph(query, graph, embeddings)\n",
    "\n",
    "        # 生成回答\n",
    "        response = generate_response(query, relevant_chunks)\n",
    "\n",
    "        # 如果有参考答案，进行对比\n",
    "        reference = None\n",
    "        comparison = None\n",
    "        if reference_answers and i < len(reference_answers):\n",
    "            reference = reference_answers[i]\n",
    "            comparison = compare_with_reference(response, reference, query)\n",
    "\n",
    "        # 将当前问题的结果添加到结果列表中\n",
    "        results.append({\n",
    "            \"query\": query,\n",
    "            \"response\": response,\n",
    "            \"reference_answer\": reference,\n",
    "            \"comparison\": comparison,\n",
    "            \"traversal_path_length\": len(traversal_path),\n",
    "            \"relevant_chunks_count\": len(relevant_chunks)\n",
    "        })\n",
    "\n",
    "        # 显示结果\n",
    "        print(f\"\\n回答: {response}\\n\")\n",
    "        if comparison:\n",
    "            print(f\"对比结果: {comparison}\\n\")\n",
    "\n",
    "    # 返回评估结果和图谱统计信息\n",
    "    return {\n",
    "        \"results\": results,\n",
    "        \"graph_stats\": {\n",
    "            \"nodes\": graph.number_of_nodes(),  # 图中的节点数量\n",
    "            \"edges\": graph.number_of_edges(),  # 图中的边数量\n",
    "            \"avg_degree\": sum(dict(graph.degree()).values()) / graph.number_of_nodes()  # 平均度数\n",
    "        }\n",
    "    }\n"
   ],
   "id": "c77cb3f12e85422b",
   "outputs": [],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-29T05:42:19.617159Z",
     "start_time": "2025-04-29T05:42:19.610833Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def compare_with_reference(response, reference, query):\n",
    "    \"\"\"\n",
    "    将生成的响应与参考答案进行比较。\n",
    "\n",
    "    Args:\n",
    "        response (str): 生成的响应\n",
    "        reference (str): 参考答案\n",
    "        query (str): 原始查询\n",
    "\n",
    "    Returns:\n",
    "        str: 比较分析结果\n",
    "    \"\"\"\n",
    "    # 系统消息，用于指导模型如何比较生成的响应与参考答案\n",
    "    system_message = \"\"\"将AI生成的回答与参考答案进行比较评估。\n",
    "评估标准包括：正确性、完整性和与查询的相关性。\n",
    "请用两到三句话（2-3句话）将生成的回答与参考答案的匹配程度进行简要分析\"\"\"\n",
    "\n",
    "    # 构建包含查询、AI生成的响应和参考答案的提示信息\n",
    "    prompt = f\"\"\"\n",
    "查询: {query}\n",
    "\n",
    "AI生成的回答:\n",
    "{response}\n",
    "\n",
    "参考答案:\n",
    "{reference}\n",
    "\n",
    "AI生成的回答与参考答案的匹配程度如何？\n",
    "\"\"\"\n",
    "\n",
    "    # 调用OpenAI API生成比较分析\n",
    "    comparison = client.chat.completions.create(\n",
    "        model=llm_model,\n",
    "        messages=[\n",
    "            {\"role\": \"system\", \"content\": system_message},  # 系统消息，用于引导助手\n",
    "            {\"role\": \"user\", \"content\": prompt}  # 用户消息，包含提示内容\n",
    "        ],\n",
    "        temperature=0.0  # 设置响应生成的温度参数\n",
    "    )\n",
    "\n",
    "    # 返回生成的比较分析内容\n",
    "    return comparison.choices[0].message.content\n"
   ],
   "id": "9fbb4b1381783765",
   "outputs": [],
   "execution_count": 13
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 在简单的文档中进行测试",
   "id": "d17dce01bede3e2d"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-29T05:56:55.980337Z",
     "start_time": "2025-04-29T05:55:17.839228Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 定义一个与 AI 相关的查询，用于测试 Graph RAG 系统\n",
    "query = \"自然语言处理中变压器（Transformers）的关键应用是什么？\"\n",
    "\n",
    "# 执行 Graph RAG 管道以处理文档并回答查询\n",
    "results = graph_rag_pipeline(pdf_path, query)\n",
    "\n",
    "# 打印由 Graph RAG 系统生成的回答\n",
    "print(\"\\n=== 回答 ===\")\n",
    "print(results[\"response\"])\n",
    "\n",
    "# 定义测试查询和参考答案以进行正式评估\n",
    "test_queries = [\n",
    "    \"与 RNN 相比，变压器（Transformers）如何处理序列数据？\"\n",
    "]\n",
    "\n",
    "# 用于评估的参考答案\n",
    "reference_answers = [\n",
    "    \"变压器（Transformers）通过使用自注意力机制而不是循环连接来处理序列数据，这使得变压器能够并行处理所有标记，而不是顺序处理。这样可以更有效地捕获长距离依赖关系，并在训练期间实现更好的并行化。与 RNN 不同，变压器不会在长序列上遇到梯度消失问题。\"\n",
    "]\n",
    "\n",
    "# 使用测试查询对 Graph RAG 系统进行正式评估\n",
    "evaluation = evaluate_graph_rag(pdf_path, test_queries, reference_answers)\n",
    "\n",
    "# 打印评估汇总统计信息\n",
    "print(\"\\n=== 评估汇总 ===\")\n",
    "print(f\"图节点数: {evaluation['graph_stats']['nodes']}\")\n",
    "print(f\"图边数: {evaluation['graph_stats']['edges']}\")\n",
    "for i, result in enumerate(evaluation['results']):\n",
    "    print(f\"\\n查询 {i+1}: {result['query']}\")\n",
    "    print(f\"遍历路径长度: {result['traversal_path_length']}\")\n",
    "    print(f\"使用的相关块数: {result['relevant_chunks_count']}\")\n"
   ],
   "id": "b62eebc0954912e1",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created 13 text chunks\n",
      "正在构建知识图谱...\n",
      "正在为片段创建嵌入...\n",
      "正在将节点添加到图中...\n",
      "正在从片段 1/13 中提取概念...\n",
      "正在从片段 2/13 中提取概念...\n",
      "正在从片段 3/13 中提取概念...\n",
      "正在从片段 4/13 中提取概念...\n",
      "正在从片段 5/13 中提取概念...\n",
      "正在从片段 6/13 中提取概念...\n",
      "正在从片段 7/13 中提取概念...\n",
      "正在从片段 8/13 中提取概念...\n",
      "正在从片段 9/13 中提取概念...\n",
      "正在从片段 10/13 中提取概念...\n",
      "正在从片段 11/13 中提取概念...\n",
      "正在从片段 12/13 中提取概念...\n",
      "正在从片段 13/13 中提取概念...\n",
      "正在在节点之间创建边...\n",
      "知识图谱已构建，包含 13 个节点和 39 条边\n",
      "正在为查询遍历图: 自然语言处理中变压器（Transformers）的关键应用是什么？\n",
      "从 3 个节点开始遍历\n",
      "图遍历找到了 9 个相关片段\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ],
      "image/png": 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     "text": [
      "\n",
      "=== 回答 ===\n",
      "根据提供的上下文，虽然没有直接提到\"变压器(Transformers)\"这个具体术语，但可以从自然语言处理(NLP)的相关内容中推断其关键应用。以下是基于上下文中NLP应用场景的总结：\n",
      "\n",
      "1. 机器翻译（上下文明确提到）\n",
      "上下文引用：\"NLP技术广泛应用于...机器翻译\"（第2章）\n",
      "\n",
      "2. 文本生成与内容创作\n",
      "上下文引用：\"人工智能用于撰写文章、生成内容和创作脚本\"（第9章）\n",
      "\n",
      "3. 情感分析\n",
      "上下文引用：\"RNN非常适合...情感分析等任务\"（第2章）\n",
      "上下文引用：\"NLP技术广泛应用于...情感分析\"（第2章）\n",
      "\n",
      "4. 聊天机器人\n",
      "上下文引用：\"NLP技术广泛应用于聊天机器人\"（第2章）\n",
      "\n",
      "5. 文本摘要\n",
      "上下文引用：\"NLP技术广泛应用于...文本摘要\"（第2章）\n",
      "\n",
      "需要说明的是，虽然Transformer架构是现代NLP的核心技术（如BERT、GPT等模型的基础），但上下文中并未明确提及该术语，上述应用是基于Transformer模型在NLP领域的实际应用与上下文描述的自然语言处理应用场景的对应关系得出的结论。\n",
      "Created 13 text chunks\n",
      "正在构建知识图谱...\n",
      "正在为片段创建嵌入...\n",
      "正在将节点添加到图中...\n",
      "正在从片段 1/13 中提取概念...\n",
      "正在从片段 2/13 中提取概念...\n",
      "正在从片段 3/13 中提取概念...\n",
      "正在从片段 4/13 中提取概念...\n",
      "正在从片段 5/13 中提取概念...\n",
      "正在从片段 6/13 中提取概念...\n",
      "正在从片段 7/13 中提取概念...\n",
      "正在从片段 8/13 中提取概念...\n",
      "正在从片段 9/13 中提取概念...\n",
      "正在从片段 10/13 中提取概念...\n",
      "正在从片段 11/13 中提取概念...\n",
      "正在从片段 12/13 中提取概念...\n",
      "正在从片段 13/13 中提取概念...\n",
      "正在在节点之间创建边...\n",
      "知识图谱已构建，包含 13 个节点和 49 条边\n",
      "\n",
      "\n",
      "=== 正在评估问题 1/1 ===\n",
      "问题: 与 RNN 相比，变压器（Transformers）如何处理序列数据？\n",
      "正在为查询遍历图: 与 RNN 相比，变压器（Transformers）如何处理序列数据？\n",
      "从 5 个节点开始遍历\n",
      "图遍历找到了 11 个相关片段\n",
      "\n",
      "回答: 根据提供的上下文，虽然文中详细介绍了RNN（循环神经网络）和深度学习的相关内容，但并未提及Transformer架构。不过基于上下文对RNN的描述和AI领域的通用知识，我可以总结两者的关键差异：\n",
      "\n",
      "1. **RNN的处理方式**（如上下文所述）：\n",
      "   - 通过\"反馈连接\"按时间步逐步处理序列数据（如文本或时间序列），信息随时间逐步传递（引用原文：\"RNN具有反馈连接，可使信息随时间持续存在\"）。\n",
      "   - 存在长程依赖问题，难以捕捉远距离的序列关系。\n",
      "\n",
      "2. **Transformer的典型特点**（基于领域知识补充）：\n",
      "   - 使用**自注意力机制**并行处理整个序列，直接建模任意位置的关系，不受距离限制。\n",
      "   - 通过**位置编码**替代RNN的时序处理，显式注入位置信息。\n",
      "   - 典型应用包括上下文未直接提及的BERT、GPT等模型，在机器翻译等领域表现优于传统RNN。\n",
      "\n",
      "3. **上下文关联的应用**：\n",
      "   - 原文提到RNN适用于\"语言翻译、语音识别\"等任务（第2章），而Transformer正是当前这些领域的SOTA架构。\n",
      "   - Transformer更擅长处理文中提到的\"发现数据中的模式\"（第2章）这一需求，尤其是长序列模式。\n",
      "\n",
      "建议结合最新的Transformer论文（如《Attention Is All You Need》）获取技术细节。上下文中关于\"深度学习进步推动突破\"（第15章）的论述也间接支持了这种架构演进的重要性。\n",
      "\n",
      "对比结果: AI生成的回答与参考答案在核心观点上高度匹配，都强调了Transformer的自注意力机制和并行处理能力，以及其解决长距离依赖问题的优势。不过AI回答更为详细，补充了位置编码等额外信息，而参考答案则更简洁直接地突出了关键差异。两者都准确指出了Transformer相对于RNN的主要改进方向。\n",
      "\n",
      "\n",
      "=== 评估汇总 ===\n",
      "图节点数: 13\n",
      "图边数: 49\n",
      "\n",
      "查询 1: 与 RNN 相比，变压器（Transformers）如何处理序列数据？\n",
      "遍历路径长度: 11\n",
      "使用的相关块数: 11\n"
     ]
    }
   ],
   "execution_count": 16
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "e1b054c69cb39bf"
  }
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